Optimal Irrigation Allocation for Large-Scale Arable Farming

Abstract

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An Adaptive Hybrid (1D-2D) Convolution-Based ShuffleNetV2 Mechanism for Irrigation Levels Prediction in Agricultural Fields With Smart IoTs

“…The LSTM is a unique kind of neural network that is particularly useful for time series analysis. Furthermore, the customers' watering needs for the crops can be predicted by the collected models [25]. To increase watersaving efficiency, the data fusion models consider real-time sensor data, weather forecasts, irrigation records, and past local weather conditions.…”

Multi-Objective Decision Support for Irrigation Systems Based on Skyline Query

“…For example, Jim'enez et al [9] used the agronomic standards of each crop, such as crop coefficient, root depth, and soil water balance to evaluate the degrees of water wastage; a top-k query was then applied to select the solution with the least wastage. Cobbenhagen et al [10] employed various crop-growth models to estimate total crop profit and found the solutions with the most profit using top-k. More recently, Zhang et al [11] used the agronomic standards of the crops, soil water, and salt balance limits to construct a two-layer multi-objective agricultural water allocation model, which evaluates economic benefits and irrigation water productivity. Solutions were also obtained in this case using a top-k query.…”